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All Journal International Journal of Evaluation and Research in Education (IJERE) JURNAL SISTEM INFORMASI BISNIS Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Ilmiah Flash Belantika Pendidikan Jurnal Manajemen Informatika Jayakarta HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Indonesian Journal of Educational Development (IJED)
S Sumarlin
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences

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Journal : JURNAL SISTEM INFORMASI BISNIS

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Implementasi Algoritma K-Nearest Neighbor Sebagai Pendukung Keputusan Klasifikasi Penerima Beasiswa PPA dan BBM Sumarlin, Sumarlin
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 5, No 1 (2015): Volume 5 Nomor 1 Tahun 2015
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1677.37 KB) | DOI: 10.21456/vol5iss1pp52-62

Abstract

In line with the growth in the academic field especially college, scholarship is a problem that is interesting to study. Several studies in the field of computers for the screening or classification scholarships have been carried out in the academic authorities to minimize the error in awarding scholarships. This study discusses the classification of PPA and BBM scholarships based on variables that have been determined by applying the k-nearest neighbor algorithm. The process of selecting awardees PPA and BBM requires a decision support system (DSS) to help provide alternative solutions. The results of the classification system will be used as a decision in awarding scholarships to students who submit. Results of testing to measure the performance of k - nearest neighbor algorithm using cross validation method, Confusion Matrix and the Receiver Operating Characteristic (ROC) curve, the accuracy obtained for PPA scholarships reached 88.33% with a value of 0.925 area under curve (AUC)  dataset of 227 records, while accuracy is obtained for fuel BBM scholarships reached 90% with a value of  0.937% AUC dataset of 183 records​​, accuracy for PPA and BBM scholarships reached 85,56% and AUC value 0,958. Because AUC values ​​were in the range of 0.9 to 1.0  the method falls within the category of very good (excellent). Keywords: Decision Support System; K-nearest neighbor; Classification; Scholarship
Co-Authors Degeng, Made Dunanda Kartika Deni Alfiando Salem Dewi Anggraini Dewi Anggraini Edwin Ariesto Umbu Malahina Heni Latuan, Yosep Jacob Menhya Snae Natun, Natasya Chalista Imanuela Punaji Setyosari Remerta Noni Naatonis Saida Ulfa